GCN-BERT and Memory Network Based Multi-Label Classification for Event Text of the Chinese Government Hotline

Abstract

In order to automatically generate multiple labels for the event text of the Chinese government hotline, this paper propose a multi-label classification framework based on graph convolutional network (GCN), BERT, and memory network. The framework consists of three modules: label count prediction module, label semantic insert module, and label selection module. In the label count prediction module, this paper constructs the event graph with the abstract meaning representation (AMR) and extract the event topic information vector with GCN. To predict the label count, this paper first use BERT to extract the event semantic information vector and then fuse it with the event topic information vector (GCN-BERT fusion vector) with a dynamic fusion gate. In the label semantic insert module, to obtain the event label candidate set, this paper uses a multi-hop memory network to store the event label semantic information, and then use the answer selection framework, which matches the GCN-BERT fusion vector with the event label semantic memory vector. In label selection module, this paper uses the label count based multi-label selection to sort the event label candidate set and guide to output the optimal multi-label set of the event. Comparison experimental results show that the proposed framework outperforms all baselines and ablation studies demonstrate the effectiveness of each module.